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Operation Ollama-First v5.0 / Phase 20+ 完整啟動 Primary + Secondary 兩台 GCP 完整對稱（10 模型 / ~67GB 各）： ✅ bge-m3:latest 1.2GB Embedding ✅ hermes3:latest 4.7GB Hermes 戰術 ✅ qwen2.5-coder:7b 4.7GB AiderHeal 既有 ✅ qwen2.5-coder:32b 19.0GB ⭐ AiderHeal 32B 升級 ✅ qwen2.5:7b-instruct 4.7GB Q&A 預設 ✅ qwen3:14b 9.3GB Q&A / Nemotron 升級 ✅ deepseek-r1:14b 9.0GB ⭐ 推理鏈備援 ✅ minicpm-v:latest 5.5GB PPT vision 主 ✅ llava:latest 4.7GB ⭐ Vision 備援 ✅ gemma3:4b 3.3GB ⭐ 輕量任務 ai_call_logger COST_TABLE 確認 4 新模型 + 2 重命名（minicpm-v / llava） - 解 logger 「unknown model cost」誤報 - 預期啟用： - qwen2.5-coder:32b → AiderHeal 大型重構（call site 將擴展） - deepseek-r1:14b → EA HITL 推理（取代部分 Gemini Pro） - llava:latest → minicpm-v 失敗備援 - gemma3:4b → sales_copy < 100 字輕量任務 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
452 lines
19 KiB
Python
452 lines
19 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/ai_call_logger.py
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統一 LLM 呼叫遙測層 (Operation Ollama-First v5.0 — Phase 1)
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依據:
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- docs/phase0_audit_report_20260503.md (34 個 LLM 呼叫點 / 11.8% 覆蓋率)
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- docs/phase1_db_design_20260503.md (ai_calls schema)
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- migrations/024_create_ai_calls_table.sql
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設計原則 (憲法級):
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1. 非阻塞: DB 寫入跑 daemon thread，主流程不等
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2. 失敗安全: DB 例外只 log warning，絕不影響 LLM 主流程
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3. PII 保護: meta 不存原始 prompt，只存 prompt_hash[:12]
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4. Kill-switch: AI_CALL_LOGGING_ENABLED=false 一鍵關閉
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5. 連續失敗 ≥ 10 次自動降級為純 logger.info
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主入口:
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- log_ai_call(...) context manager (推薦)
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- logged_ai_call(...) decorator (簡單一行 LLM call)
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"""
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from __future__ import annotations
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import hashlib
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import inspect
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import logging
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import os
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import threading
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import time
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from contextlib import contextmanager
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from functools import wraps
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from typing import Any, Callable, Dict, Optional
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logger = logging.getLogger(__name__)
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# ─────────────────────────────────────────────────────────────────────────────
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# 成本表 (USD per 1M tokens)
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# 依據 phase0 audit + 各 provider 官方定價，Ollama 全部 0
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# ─────────────────────────────────────────────────────────────────────────────
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COST_TABLE: Dict[str, Dict[str, float]] = {
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# Gemini
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'gemini-2.5-flash': {'in': 0.075, 'out': 0.30},
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'gemini-2.5-pro': {'in': 1.25, 'out': 10.0},
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'gemini-2.0-flash': {'in': 0.075, 'out': 0.30},
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'gemini-1.5-flash': {'in': 0.075, 'out': 0.30},
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# NVIDIA NIM (配額制，免費 tier 全 0)
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'meta/llama-3.1-8b-instruct': {'in': 0.0, 'out': 0.0},
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'meta/llama-3.3-70b-instruct': {'in': 0.0, 'out': 0.0},
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'nvidia/llama-3.3-nemotron-super-49b-v1.5': {'in': 0.0, 'out': 0.0},
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'deepseek-ai/deepseek-v3.2': {'in': 0.0, 'out': 0.0},
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# Claude (Anthropic) — 2026-05 市場價（USD per 1M tokens）
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'claude-opus-4-7': {'in': 15.0, 'out': 75.0}, # 程式碼 #1，Arena Elo 1548
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'claude-sonnet-4-6': {'in': 3.0, 'out': 15.0}, # agentic 平衡
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'claude-haiku-4-5': {'in': 0.8, 'out': 4.0}, # 輕量快速
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# Ollama 自架 (全 0；Phase 8/13 GCP 拉模型陸續加入)
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'hermes3:latest': {'in': 0.0, 'out': 0.0},
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'qwen2.5-coder:7b': {'in': 0.0, 'out': 0.0},
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'qwen2.5-coder:32b': {'in': 0.0, 'out': 0.0}, # 2026-05-04 Primary+Secondary 已拉（19GB）AiderHeal 32B
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'qwen2.5:7b-instruct': {'in': 0.0, 'out': 0.0}, # Phase 3 A7 OpenClaw Q&A 預設
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'qwen3:14b': {'in': 0.0, 'out': 0.0}, # Phase 3 A9 Nemotron + A7 升級
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'qwen2-vl:7b': {'in': 0.0, 'out': 0.0}, # Phase 13 PPT vision（Ollama registry 暫無，用 minicpm-v 代替）
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'deepseek-r1:14b': {'in': 0.0, 'out': 0.0}, # 2026-05-04 已拉（9GB），推理鏈備援
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'gemma3:4b': {'in': 0.0, 'out': 0.0}, # 2026-05-04 已拉（3.3GB），輕量任務
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'minicpm-v:latest': {'in': 0.0, 'out': 0.0}, # Phase 14 PPT vision（5.5GB）
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'llava:latest': {'in': 0.0, 'out': 0.0}, # 2026-05-04 已拉（4.7GB），Vision 備援
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'llama3.1:8b': {'in': 0.0, 'out': 0.0},
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'bge-m3:latest': {'in': 0.0, 'out': 0.0},
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}
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# ─────────────────────────────────────────────────────────────────────────────
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# 環境開關 + Kill-switch
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# ─────────────────────────────────────────────────────────────────────────────
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def _is_logging_enabled() -> bool:
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"""環境變數即時讀取 (允許 runtime toggle)"""
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val = os.environ.get('AI_CALL_LOGGING_ENABLED', 'true').strip().lower()
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return val not in ('false', '0', 'no', 'off')
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# 連續失敗門檻；超過後降級為純 logger.info，不再嘗試 DB
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_MAX_CONSECUTIVE_FAILURES = 10
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_failure_counter_lock = threading.Lock()
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_failure_state = {'count': 0, 'killed': False}
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def _record_failure() -> None:
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with _failure_counter_lock:
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_failure_state['count'] += 1
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if _failure_state['count'] >= _MAX_CONSECUTIVE_FAILURES and not _failure_state['killed']:
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_failure_state['killed'] = True
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logger.error(
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"[AICallLogger] consecutive write failures hit %d — kill-switch ON, "
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"downgrading to logger.info only",
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_MAX_CONSECUTIVE_FAILURES,
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)
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def _record_success() -> None:
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with _failure_counter_lock:
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if _failure_state['count'] > 0:
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_failure_state['count'] = 0
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def _is_killed() -> bool:
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with _failure_counter_lock:
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return _failure_state['killed']
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def _reset_kill_switch() -> None:
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"""測試專用：重置 kill-switch 狀態。"""
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with _failure_counter_lock:
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_failure_state['count'] = 0
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_failure_state['killed'] = False
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# ─────────────────────────────────────────────────────────────────────────────
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# 內部狀態容器
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# ─────────────────────────────────────────────────────────────────────────────
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class _CallState:
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"""單次 LLM 呼叫的遙測狀態容器。"""
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__slots__ = (
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'caller', 'provider', 'model', 'request_id',
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'input_tokens', 'output_tokens',
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'duration_ms', 'status', 'fallback_to',
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'cost_usd', 'cache_hit', 'rag_hit',
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'error', 'meta',
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)
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def __init__(self, caller: str, provider: str, model: str,
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request_id: Optional[str], meta: Dict[str, Any]):
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# Phase 16 (2026-05-04)：caller_registry 強制驗證（critic-A11 L4 修補）
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# 不在 registry 不 raise（保留擴展彈性），只 log warning 提醒新增 ADR
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try:
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from services.llm_caller_registry import assert_known_caller
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assert_known_caller(caller, strict=False)
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except ImportError:
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pass # registry 不可用時不阻擋（向下相容）
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self.caller = caller
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self.provider = provider
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self.model = model
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self.request_id = request_id
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self.input_tokens = 0
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self.output_tokens = 0
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self.duration_ms: Optional[int] = None
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self.status: Optional[str] = None
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self.fallback_to: Optional[str] = None
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self.cost_usd = 0.0
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self.cache_hit = False
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self.rag_hit = False
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self.error: Optional[str] = None
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self.meta: Dict[str, Any] = dict(meta) if meta else {}
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# ── caller 操作 API ──────────────────────────────────────────────
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def set_tokens(self, input: int = 0, output: int = 0) -> None:
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"""設定 token 數。容錯 None / 非整數。"""
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try:
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self.input_tokens = int(input or 0)
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except (TypeError, ValueError):
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self.input_tokens = 0
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try:
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self.output_tokens = int(output or 0)
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except (TypeError, ValueError):
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self.output_tokens = 0
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def set_cache_hit(self, hit: bool = True) -> None:
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self.cache_hit = bool(hit)
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def set_rag_hit(self, hit: bool = True) -> None:
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self.rag_hit = bool(hit)
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def fallback_to_caller(self, target_caller: str) -> None:
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"""主路徑失敗、觸發下游 caller 接手。下游本身會另寫一筆 ok/error。"""
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self.fallback_to = (target_caller or '')[:64]
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self.status = 'fallback'
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# 別名：與設計文 spec 對齊
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fallback_to_target = fallback_to_caller
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def set_error(self, msg: str) -> None:
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self.error = (msg or '')[:2000]
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self.status = 'error'
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def set_status(self, status: str) -> None:
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self.status = (status or '')[:16]
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def set_prompt_hash(self, prompt: Optional[str]) -> None:
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"""安全地將 prompt 轉成 hash 存入 meta（PII 保護）。"""
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if prompt:
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digest = hashlib.sha256(prompt.encode('utf-8', errors='replace')).hexdigest()
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self.meta['prompt_hash'] = digest[:12]
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def add_meta(self, key: str, value: Any) -> None:
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if key:
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self.meta[key] = value
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# ─────────────────────────────────────────────────────────────────────────────
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# 主入口 1: context manager
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# ─────────────────────────────────────────────────────────────────────────────
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@contextmanager
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def log_ai_call(
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caller: str,
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provider: str,
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model: str,
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request_id: Optional[str] = None,
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meta: Optional[Dict[str, Any]] = None,
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):
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"""
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使用範例:
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with log_ai_call('hermes_analyst', 'gcp_ollama', 'hermes3:latest') as ctx:
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response = ollama.generate(...)
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ctx.set_tokens(input=response['prompt_eval_count'],
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output=response['eval_count'])
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ctx.set_cache_hit(False)
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# 失敗時 ctx.set_error('timeout') / ctx.fallback_to_caller('111_ollama')
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紀律:
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- 永遠不影響主流程：例外會 re-raise，但 logger 寫入是 fire-and-forget
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- 若 AI_CALL_LOGGING_ENABLED=false → 仍 yield ctx（API 一致），但跳過寫入
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"""
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state = _CallState(caller, provider, model, request_id, meta or {})
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start = time.monotonic()
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try:
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yield state
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# 沒例外 → 若 caller 自己沒設 status，預設 ok
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if state.status is None:
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state.status = 'ok'
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except Exception as e:
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state.status = 'error'
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if not state.error:
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state.error = f"{type(e).__name__}: {str(e)[:1500]}"
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raise
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finally:
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state.duration_ms = int((time.monotonic() - start) * 1000)
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try:
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_async_write(state)
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except Exception as exc: # pragma: no cover — 寫入 thread 啟動失敗
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logger.warning("[AICallLogger] async dispatch failed: %s", exc)
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# ─────────────────────────────────────────────────────────────────────────────
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# 主入口 2: decorator
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# ─────────────────────────────────────────────────────────────────────────────
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def logged_ai_call(
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caller: str,
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provider: str,
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model: Optional[str] = None,
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model_extractor: Optional[Callable[[tuple, dict], str]] = None,
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):
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"""
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使用範例:
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@logged_ai_call(caller='sales_copy', provider='gcp_ollama',
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model_extractor=lambda a, kw: kw.get('model', 'llama3.1:8b'))
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def generate_copy(...):
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return ollama.generate(model='llama3.1:8b', ...)
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Args:
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caller: ai_calls.caller 白名單字串
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provider: ai_calls.provider
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model: 靜態模型名（與 model_extractor 二擇一）
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model_extractor: 從 (args, kwargs) 解析 model 名（動態優先）
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注意:
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- decorator 不知道 token 數；若需精準 token，請改用 log_ai_call context manager
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- 例外會 re-raise，狀態自動標 error
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"""
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def deco(fn: Callable):
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@wraps(fn)
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def wrapper(*args, **kwargs):
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try:
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resolved_model = (
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model_extractor(args, kwargs) if model_extractor else (model or 'unknown')
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)
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except Exception:
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resolved_model = model or 'unknown'
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with log_ai_call(caller, provider, resolved_model) as ctx:
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result = fn(*args, **kwargs)
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# 嘗試從 result 自動抽 tokens（best-effort，失敗不影響主流程）
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try:
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_auto_extract_tokens(ctx, result)
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except Exception:
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pass
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return result
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return wrapper
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return deco
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def _auto_extract_tokens(ctx: _CallState, result: Any) -> None:
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"""從常見 LLM response 形態自動抽 token（best-effort）。"""
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if result is None:
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return
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# dict (Ollama / NIM raw)
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if isinstance(result, dict):
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usage = result.get('usage') or {}
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if usage:
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ctx.set_tokens(
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input=usage.get('prompt_tokens') or usage.get('input_tokens') or 0,
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output=usage.get('completion_tokens') or usage.get('output_tokens') or 0,
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)
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return
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# Ollama: prompt_eval_count / eval_count
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if 'eval_count' in result or 'prompt_eval_count' in result:
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ctx.set_tokens(
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input=result.get('prompt_eval_count', 0),
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output=result.get('eval_count', 0),
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)
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return
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# ─────────────────────────────────────────────────────────────────────────────
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# 異步寫入 (fire-and-forget)
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# ─────────────────────────────────────────────────────────────────────────────
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def _async_write(state: _CallState) -> None:
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"""放到 daemon thread 寫，主流程不阻塞。
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若 AI_CALL_LOGGING_ENABLED=false → 直接跳過。
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若 kill-switch 觸發 → 退化為 logger.info。
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"""
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if not _is_logging_enabled():
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return
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if _is_killed():
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# 降級模式：純 log，不再碰 DB
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logger.info(
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"[AICall|killed] caller=%s provider=%s model=%s status=%s "
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"tokens=%s/%s duration=%sms",
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state.caller, state.provider, state.model, state.status,
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state.input_tokens, state.output_tokens, state.duration_ms,
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)
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return
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threading.Thread(
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target=_write_to_db,
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args=(state,),
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name=f"ai-call-log-{state.caller}",
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daemon=True,
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).start()
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def _write_to_db(state: _CallState) -> None:
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"""try/except 全包；DB 掛了只 log warning 不爆炸。"""
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try:
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from sqlalchemy import text
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from database.manager import get_session
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cost = _calc_cost(state.model, state.input_tokens, state.output_tokens)
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meta_json = _safe_meta_json(state.meta)
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session = get_session()
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try:
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session.execute(
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text("""
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INSERT INTO ai_calls (
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caller, provider, model,
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input_tokens, output_tokens, duration_ms,
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status, fallback_to, cost_usd,
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cache_hit, rag_hit, request_id,
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error, meta
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) VALUES (
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:caller, :provider, :model,
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:input_tokens, :output_tokens, :duration_ms,
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:status, :fallback_to, :cost_usd,
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:cache_hit, :rag_hit, :request_id,
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:error, CAST(:meta AS JSONB)
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)
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"""),
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{
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'caller': state.caller[:64] if state.caller else 'unknown',
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'provider': (state.provider or 'unknown')[:32],
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'model': (state.model or 'unknown')[:128],
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'input_tokens': int(state.input_tokens or 0),
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'output_tokens': int(state.output_tokens or 0),
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'duration_ms': state.duration_ms,
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'status': (state.status or 'ok')[:16],
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'fallback_to': state.fallback_to,
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'cost_usd': cost,
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'cache_hit': bool(state.cache_hit),
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'rag_hit': bool(state.rag_hit),
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'request_id': state.request_id,
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'error': state.error,
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'meta': meta_json,
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},
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)
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session.commit()
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_record_success()
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except Exception:
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session.rollback()
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raise
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finally:
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session.close()
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except Exception as e:
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_record_failure()
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logger.warning(
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"[AICallLogger] write failed (caller=%s provider=%s): %s",
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state.caller, state.provider, e,
|
||
)
|
||
|
||
|
||
def _calc_cost(model: str, in_tokens: int, out_tokens: int) -> float:
|
||
"""依 COST_TABLE 計算成本；未知 model log warning 並回 0。"""
|
||
if not model:
|
||
return 0.0
|
||
rate = COST_TABLE.get(model)
|
||
if rate is None:
|
||
# NIM 配額制走免費 tier，常見 nvidia/* meta/* deepseek-* 視為 0
|
||
prefix_zero = ('meta/', 'nvidia/', 'deepseek-')
|
||
if any(model.startswith(p) for p in prefix_zero):
|
||
return 0.0
|
||
logger.warning("[AICallLogger] unknown model cost: %s, default 0", model)
|
||
return 0.0
|
||
in_t = max(0, int(in_tokens or 0))
|
||
out_t = max(0, int(out_tokens or 0))
|
||
cost = (in_t * rate['in'] + out_t * rate['out']) / 1_000_000
|
||
# NUMERIC(10,6) 上限 9999.999999；極端 case 截斷避免 overflow
|
||
if cost < 0:
|
||
return 0.0
|
||
return round(min(cost, 9999.999999), 6)
|
||
|
||
|
||
def _safe_meta_json(meta: Dict[str, Any]) -> str:
|
||
"""meta 序列化為 JSON 字串；失敗時回 '{}'。"""
|
||
import json
|
||
if not meta:
|
||
return '{}'
|
||
try:
|
||
return json.dumps(meta, ensure_ascii=False, default=str)
|
||
except Exception as e:
|
||
logger.warning("[AICallLogger] meta json dump failed: %s", e)
|
||
return '{}'
|
||
|
||
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
# 工具：caller 自動推斷（caller 沒給時用）
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
def infer_caller_from_stack(default: str = 'unknown') -> str:
|
||
"""從 inspect.stack() 推斷 caller（取上 1 層的 module 名末段）。"""
|
||
try:
|
||
frame = inspect.stack()[2]
|
||
module = inspect.getmodule(frame.frame)
|
||
if module and module.__name__:
|
||
return module.__name__.split('.')[-1][:64]
|
||
except Exception:
|
||
pass
|
||
return default
|